Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add linkfox-ai/linkfox-skills --skill linkfox-seerfar-ozon-keyword-back-searchgit clone --depth 1 https://github.com/linkfox-ai/linkfox-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-seerfar-ozon-keyword-back-search)<a href="https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-seerfar-ozon-keyword-back-search"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-seerfar-ozon-keyword-back-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-seerfar-ozon-keyword-back-search"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-seerfar-ozon-keyword-back-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 108 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00304 | $0.03535 |
| Opus 5 | $0.00152 | $0.01767 |
| Sonnet 5 | $0.00061 | $0.00707 |
| Haiku 4.5 | $0.00030 | $0.00353 |
Grade A, and why
linkfox-seerfar-ozon-keyword-back-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Seerfar Ozon Keyword Back-Search
This skill reverse-looks-up Ozon search keywords by a list of product SKU IDs in the Seerfar analytics database: pass up to 20 SKUs (your own listing or a competitor's) and it returns the search terms those products appear under — organic and/or ad — each enriched with a full market profile (search volume, 30-day growth, product/seller/competitor counts, average price, conversion concentration, top products, plus per-term organic/ad channel, natural rank, exposure, and conversion in the dimension object). It is the starting point for Ozon keyword reverse lookup, listing-title optimization, and competitor traffic-word discovery.
Core Concepts
SKU-driven, not keyword-driven: unlike keyword mining (expand from a seed term) or market keyword search (browse the whole market), this endpoint takes skuIds and returns the search terms those specific products rank for. The direction is product → keywords (reverse).
hasVariant is required: every request must declare whether to exclude variants — 0 keep variants, 1 exclude variants. Pick 1 when you want de-duplicated keyword coverage for a parent listing.
Natural vs ad terms: type filters the search-term channel — ["0"] organic (自然搜索词) only, ["1"] ad (广告搜索词) only; omit to get both. Combine with the naturalRank / adRank range filters to qualify positioning.
Back-search metrics live in dimension: each returned term carries a dimension object with the reverse-lookup-specific metrics — type (0 organic / 1 ad), naturalRank (the SKU's natural rank for that term), exposure (exposure share, 0–1), conversion (conversion rate, 0–1), and x (opaque position indicator). The input filters type / naturalRank / adRank / exposure / conversion filter on these same per-term values. Note: relevancy is defined in the schema but is not returned by this endpoint.
Platform coverage: each keyword record carries a platform field (0 = Ozon, 1 = Wildberries). The dataset is Ozon-centric; Wildberries rows appear where available. There is no input to restrict the platform — filter client-side if needed.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 164 lines · 304 tokens per session scan A 782c86ff9851
linkfox-seerfar-ozon-keyword-back-search is a skill published in the GitHub repository linkfox-ai/linkfox-skills (101 stars, last pushed 22d ago), licensed MIT. It adds 304 tokens to every session and 3,535 once invoked, about $0.0015 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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